Fast, small, efficient JavaScript memoization lib to memoize JS functions. 

Fast, small, efficient JavaScript memoization lib to memoize JS functions

thinkloop

memoizerific

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  "description": "Fast, small, efficient JavaScript memoization lib to memoize JS functions",

  "homepage": "https://github.com/thinkloop/memoizerific#readme",

    "url": "git+https://github.com/thinkloop/memoizerific.git"

    "url": "https://github.com/thinkloop/memoizerific/issues"

    "test": "echo '****************'`date +%r`'********************' && npm run -s test:map && npm run -s test:similar",

    "publish_to_npm": "npm version patch && git push && git push --tags && npm publish",

    "test:map": "FORCE_SIMILAR_INSTEAD_OF_MAP=false JASMINE_CONFIG_PATH=./jasmine.json jasmine",

    "test:similar": "FORCE_SIMILAR_INSTEAD_OF_MAP=true JASMINE_CONFIG_PATH=./jasmine.json jasmine",

    "build:all": "npm run -s build:browserify | tee memoizerific.js | npm run -s build:minify | tee memoizerific.min.js | npm run -s build:gzip > memoizerific.min.gzip.js",

    "build:browserify": "browserify src/memoizerific.js -t [envify purge] --node --detectGlobals false --standalone memoizerific | derequire",

    "build:minify": "uglifyjs --compress drop_console,unused=false --mangle --screw-ie8",

[![Build Status](https://travis-ci.org/thinkloop/memoizerific.svg?branch=master)](https://travis-ci.org/thinkloop/memoizerific)

Fast (see benchmarks), small (1k min/gzip), efficient, JavaScript memoization lib to memoize JS functions.

Uses JavaScript's new [Map()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Map) object for instant lookups, or a [performant polyfill](https://github.com/thinkloop/map-or-similar) if Map is not available - does not do expensive serialization or string manipulation.

Fully supports multiple complex arguments.

Implements least recently used (LRU) caching to maintain only the most recent results.

Memoization is the process of caching function results so that they can be returned cheaply

without re-execution, when the function is called again with the same arguments.

This is especially useful with the rise of [redux-philosophy](https://github.com/rackt/redux),

and the push to calculate derived data on the fly to maintain minimal state.

Or use one of the compiled distributions compatible in any environment (UMD):

- [memoizerific.js](https://raw.githubusercontent.com/thinkloop/memoizerific/master/memoizerific.js)

- [memoizerific.min.js](https://raw.githubusercontent.com/thinkloop/memoizerific/master/memoizerific.min.js) (minified)

- [memoizerific.min.gzip.js](https://github.com/thinkloop/memoizerific/raw/master/memoizerific.min.gzip.js) (minified + gzipped)

var memoizerific = require('memoizerific');

var myExpensiveFunctionMemoized = memoizerific(50)(function(arg1, arg2, arg3) {

    // so many long expensive calls in here

myExpensiveFunctionMemoized(1, 2, 3); // that took long to process

myExpensiveFunctionMemoized(1, 2, 3); // wow, that one was instant!

myExpensiveFunctionMemoized(2, 3, 4); // expensive again :(

myExpensiveFunctionMemoized(2, 3, 4); // woah, this one was dirt cheap

var complexArg1 = { a: { b: { c: 99 }}}, // hairy nested object

    complexArg2 = [{ z: 1}, { q: [{ x: 3 }]}], // objects within arrays within arrays

    complexArg3 = new Map([['d', 55],['e', 66]]), // new Map object

    complexArg4 = new Set(); // new Set object

myExpensiveFunctionMemoized(complexArg1, complexArg2, complexArg3, complexArg4); // slow

myExpensiveFunctionMemoized(complexArg1, complexArg2, complexArg3, complexArg4); // instant!

`limit:` the max number of results to cache.

memoizerific(1)(function(arg1){}); // memoize the last result for a given argument

memoizerific(10000)(function(arg1, arg2){}); // memoize the last 10,000 unique argument combinations

memoizerific(0)(function(arg1){}); // memoize infinity results (not recommended)

The cache works using LRU logic, purging the least recently used results when the limit is reached.

var myMemoized = memoizerific(1)(function(arg1) {});

myMemoized(1); // function runs, result is cached

myMemoized(1); // cached result is returned

myMemoized(2); // function runs again, new result is cached, old cached result is purged

myMemoized(2); // new cached result is returned

Arguments are compared using strict equality.

A complex object will only trigger a cache hit if it refers to the exact same object in memory,

not just another object that has similar properties.

For example, the following code won't produce a cache hit even though the objects look the same:

myMemoized({ a: true }); // function runs

myMemoized({ a: true }); // not cached, runs again

This is because a new object is being created on each invocation, rather than the same object being passed in.

To _fix_ this, the argument can be saved in a common variable:

- :heavy_multiplication_x: [Memoizejs](https://github.com/addyosmani/memoize.js) (@addyosmani)

:heavy_multiplication_x: [Memoizejs](https://github.com/addyosmani/memoize.js) (@addyosmani)

- :heavy_multiplication_x: [Memoize-strict](https://github.com/jshanson7/memoize-strict) (@jshanson7)

:heavy_multiplication_x: [Memoize-strict](https://github.com/jshanson7/memoize-strict) (@jshanson7)

- :heavy_multiplication_x: [Deep-memoize](https://github.com/rjmk/deep-memoize) (@rjmk)

:heavy_multiplication_x: [Deep-memoize](https://github.com/rjmk/deep-memoize) (@rjmk)

- :heavy_multiplication_x: [Mem](https://github.com/sindresorhus/mem) (@sindresorhus)

:heavy_multiplication_x: [Mem](https://github.com/sindresorhus/mem) (@sindresorhus)

Three libs with reasonable traction seem to meet the basic criteria:

- [Memoizee](https://github.com/medikoo/memoizee) (@medikoo)

- [LRU-Memoize](https://github.com/erikras/lru-memoize) (@erikras)

- ~~[LRU-Memoize](https://github.com/neilk/lru-memoize) (@neilk)~~

:heavy_check_mark: [Memoizee](https://github.com/medikoo/memoizee) (@medikoo)

After some quick testing, however, we found the library by @neilk to be producing incorrect results, leaving only two viable candidates.

:heavy_check_mark: [LRU-Memoize](https://github.com/erikras/lru-memoize) (@erikras)

:heavy_check_mark: ~~[LRU-Memoize](https://github.com/neilk/lru-memoize) (@neilk)~~

After some quick testing, however, we found the last library produced incorrect results, leaving only two viable candidates.

This library is intended for real-world use-cases, and is therefore benchmarked using large, complex, real-world data.

There are enough fibonacci solvers out there.

    { a: 1, b: [{ c: 2, d: { e: 3 }}] }, // 1st argument

    [{ x: 'x', q: 'q', }, { b: 8, c: 9 }, { b: 2, c: [{x: 5, y: 3}, {x: 2, y: 7}] }, { b: 8, c: 9 }, { b: 8, c: 9 }], // 2nd argument

We generated sets of thousands of random argument combinations of varying variance (to increase and decrease cache hits and misses) and fed

Following is data from 5000 iterations of each test on firefox 44:

| :--------: | :------: | :---------------------------: | :---------: | :------: | :----------: | :------: |

| 10         | 2        | 99%                           | 19ms        | 31ms     | **10ms**     | _90%_    |

| 10         | 2        | 62%                           | 212ms       | 319ms    | **172ms**    | _23%_    |

| 10         | 2        | 7%                            | 579ms       | 617ms    | **518ms**    | _12%_    |

|            |          |                               |             |          |              |          |

| 100        | 2        | 99%                           | 137ms       | 37ms     | **20ms**     | _85%_    |

| 100        | 2        | 69%                           | 696ms       | 245ms    | **161ms**    | _52%_    |

| 100        | 2        | 10%                           | 1,057ms     | 649ms    | **527ms**    | _23%_    |

| 500        | 4        | 95%                           | 476ms       | 67ms     | **62ms**     | _8%_     |

| 500        | 4        | 36%                           | 2,642ms     | 703ms    | **594ms**    | _18%_    |

| 500        | 4        | 11%                           | 3,619ms     | 880ms    | **725ms**    | _21%_    |

| 1000       | 8        | 95%                           | 1,009ms     | **52ms** | 65ms         | _25%_    |

| 1000       | 8        | 14%                           | 10,477ms    | 659ms    | **635ms**    | _4%_     |

| 1000       | 8        | 1%                            | 6,943ms     | 1,501ms  | **1,466ms**  | _2%_     |

Cache Size                    : The maximum number of results to cache.

		{
		"name": "memoizerific",
		"version": "1.11.1",
		"version": "1.11.2",
		"description": "Fast, small, efficient JavaScript memoization lib to memoize JS functions",
		@@ -5,0 +5,0 @@ "author": "Baz <baz@thinkloop.com>",

		@@ -143,20 +143,20 @@ # Memoizerific.js

		- :heavy_multiplication_x: [Memoizejs](https://github.com/addyosmani/memoize.js) (@addyosmani)
		:heavy_multiplication_x: [Memoizejs](https://github.com/addyosmani/memoize.js) (@addyosmani)

		- :heavy_multiplication_x: [Memoize-strict](https://github.com/jshanson7/memoize-strict) (@jshanson7)
		:heavy_multiplication_x: [Memoize-strict](https://github.com/jshanson7/memoize-strict) (@jshanson7)

		- :heavy_multiplication_x: [Deep-memoize](https://github.com/rjmk/deep-memoize) (@rjmk)
		:heavy_multiplication_x: [Deep-memoize](https://github.com/rjmk/deep-memoize) (@rjmk)

		- :heavy_multiplication_x: [Mem](https://github.com/sindresorhus/mem) (@sindresorhus)
		:heavy_multiplication_x: [Mem](https://github.com/sindresorhus/mem) (@sindresorhus)

		Three libs with reasonable traction seem to meet the basic criteria:

		- [Memoizee](https://github.com/medikoo/memoizee) (@medikoo)
		- [LRU-Memoize](https://github.com/erikras/lru-memoize) (@erikras)
		- ~~[LRU-Memoize](https://github.com/neilk/lru-memoize) (@neilk)~~
		:heavy_check_mark: [Memoizee](https://github.com/medikoo/memoizee) (@medikoo)

		After some quick testing, however, we found the library by @neilk to be producing incorrect results, leaving only two viable candidates.
		:heavy_check_mark: [LRU-Memoize](https://github.com/erikras/lru-memoize) (@erikras)

		Time to test performance.
		:heavy_check_mark: ~~[LRU-Memoize](https://github.com/neilk/lru-memoize) (@neilk)~~

		After some quick testing, however, we found the last library produced incorrect results, leaving only two viable candidates.

		## Benchmarks
		@@ -163,0 +163,0 @@

Improved metrics